Churn Prediction using Dynamic RFM-Augmented node2vec
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1 Churn Predictin using Dynamic RFM-Augmented nde2vec Sandra Mitrvić, Jchen de Weerdt, Bart Baesens & Wilfried Lemahieu Department f Decisin Sciences and Infrmatin Management, KU Leuven 18 September 2017, DyN Wrkshp, ECML 2017 Skpje, Macednia
2 Outline Intrductin Mtivatin Methdlgy Experimental evaluatin Results Cnclusin Future wrk 2 Churn Predictin using Dynamic RFM-Augmented nde2vec
3 Intrductin Churn predictin (CP) Predict which custmers are ging t leave cmpany s services Still cnsidered as tpmst challenge fr Telcs (FCC reprt, 2009) Due t acquisitin/retentin cst imbalance Different types f data used fr CP Subscriptin, sci-demgraphic, custmer cmplaints etc. Mre recently: Call Detail Recrds (CDRs) CDRs -> call graphs 3 Churn Predictin using Dynamic RFM-Augmented nde2vec
4 Call graph featurizatin Extracting infrmative features frm (call) graphs An intricate prcess, due t: Cmplex structure / different types f infrmatin Tplgy-based (structural) Interactin-based (as part f custmer behavir) Edge weights quantifying custmer behavir Dynamic aspect Call graph are time-evlving Bth ndes and edges vlatile Churn = lack f activity 4 Churn Predictin using Dynamic RFM-Augmented nde2vec
5 Mtivatin Prblems identified (w.r.t. current literature) Nt many studies accunt fr dynamic aspects f call netwrks Our gal Especially nt jintly with interactin and structural features Structural features are under-explited Due t high cmputatinal time in large graphs (e.g. betweenness centrality) And withut using ad-hc handcrafted features N featurizatin methdlgy Dataset dependent Perfrming hlistic featurizatin f call graphs Incrprating bth interactin and structural infrmatin Aviding/reducing feature handcrafting While als capturing the dynamic aspect f the netwrk 5 Churn Predictin using Dynamic RFM-Augmented nde2vec
6 Methdlgy Hw d we address these gals? G1: Incrprating bth interactin and structural infrmatin Devise different peratinalizatins f RFM features and nvel RFMaugmented call graph architectures G2: Aviding/reducing feature handcrafting Opt fr representatin learning G3: Capturing the dynamic aspect f the netwrk Slice riginal netwrk int weekly snapshts 6 Churn Predictin using Dynamic RFM-Augmented nde2vec
7 Integrating interactin and structural infrmatin Interactins (current literature) Interactins (this wrk) Usually delineated with RFM (Recency,Frequency,Mnetary) variables Benefits: Simple Yet still with gd predictive pwer Many different peratinalizatins Different dimensins Different granularities Summary RFM (RFM s ) Detailed RFM (RFM d ) Directin & destinatin sliced: X ut_h, X ut_, X in, X {R,F,M} Churn RFM (RFM ch ) Only w.r.t. churners 7 Churn Predictin using Dynamic RFM-Augmented nde2vec
8 RFM-Augmented netwrks Original tplgy extended By intrducing artificial ndes based n RFM Structural infrmatin partially preserved Each f R, F, M partitined int 5 quantiles One artificial nde assigned t each quantile Interactin inf embedded thrugh extended tplgy RFM features Netwrk tplgy 4 augmented netwrks RFM s RFM s RFM ch RFM d + RFM d RFM ch AG s AG s+ch AG d AG d+ch 8 Churn Predictin using Dynamic RFM-Augmented nde2vec
9 Representatin learning Nde2vec Idea: Bring the representatins f the wrds frm the same cntext C clse (brrwed frm SkipGram) Learn f, f: V -> R d, d<< V s.t. max Σ v in V lg Pr(C v f(v)) Definitin f cntext in graph setting? Neighbrhds/Randm walks Of which rder? Hw t perfrm a walk? Flexible walks using additinal parameters Return parameter p In-ut parameter q Cming frm i, prbability t transitin w jk, if d ik = 1 frm j t k is: w jk /p, if d ik = 0 w jk /q, if d ik = 2 9 Figure surce: Grver & Leskvec, 2016 Churn Predictin using Dynamic RFM-Augmented nde2vec
10 Nde2vec -> scalable nde2vec Nde2vec Accunts bth fr previus and current nde Additinal parameters (p,q) T make walks efficient, requires precmputatin f prbability transitins: On nde level (1 st time) Scalable nde2vec Accunts nly fr current nde N additinal parameters Requires precmputatin f prbability transitins nly n nde level Alias sampling retained On edge level (successive) Alias sampling used fr efficient sampling reduces O(n) t O(1) Therefre, scales well even n large graphs! Hwever, des nt scale well n large graphs! (ur case ~ 40M edges) 10 Churn Predictin using Dynamic RFM-Augmented nde2vec
11 Dynamic graphs Different definitins (current literature) G = (V, E, T) G = (V, E, T, ΔT) G = (V, E, T, σ, ΔT) Standard apprach Cnsider several static snapshts f a dynamic graph Our setting Mnthly call graph G = (V, E) -> Fur tempral graphs G i = (V i, E i, w i ), i =1,..,4 11 Churn Predictin using Dynamic RFM-Augmented nde2vec
12 Methdlgy Graphical verview 12 Churn Predictin using Dynamic RFM-Augmented nde2vec
13 Experimental Evaluatin (1/2) One prepaid, ne pstpaid dataset 4 mnths data (nly CDRs) Undirected netwrks Mdel Lgistic regressin with L 2 regul. (10-fld CV fr tuning hyperparam.) Evaluatin AUC, lift (0.5%) Parameter Scalable nde2vec # walks 10 walk length 30 cntext size 10 # dimen. 128 # iteratins 5 13 Churn Predictin using Dynamic RFM-Augmented nde2vec
14 Experimental Evaluatin (2/2) Research questins RQ1: D features taking int accunt dynamic aspects perfrm better than static nes? RQ2: D RFM-augmented netwrk cnstructins imprve predictive perfrmance? RQ3: Des the granularity f interactin infrmatin (summary, summary +churn, detailed, detailed+churn) influence the predictive perfrmance? Experiments RFM s stat. vs. RFM s dyn. vs. AG s stat. vs. AG s dyn. -> summary RFM s+ch stat. vs. RFM s+ch dyn. vs. AG s+ch stat. vs. AG s+ch dyn. -> summary+churn RFM d stat. vs. RFM d dyn. vs. AG d stat. vs. AG d dyn. -> detailed RFM d+ch stat. vs. RFM d+ch dyn. vs. AG d+ch stat. vs. Ag d+ch dyn. -> detailed+churn 14 Churn Predictin using Dynamic RFM-Augmented nde2vec
15 Experimental results (1/2) Prepaid RQ1 Answer: Dynamic better than static! RQ2 Answer: RFM-augmented netwrks imprve predictive perfrmance RQ3 Answer: Best perfrming interactin granularity is: summary+churn Secnd best: detailed+churn 15 Churn Predictin using Dynamic RFM-Augmented nde2vec
16 Experimental results (2/2) Pstpaid RQ1 Answer: Dynamic better than static! RQ2 Answer: RFM-augmented netwrks imprve predictive perfrmance RQ3 Answer: Best perfrming interactin granularity is summary+churn Secnd best: summary 16 Churn Predictin using Dynamic RFM-Augmented nde2vec
17 Cnclusin We design RFM-augmentatins f riginal graphs Enable cnjining interactin and structural infrmatin We devise a scalable adaptin f the riginal nde2vec apprach Relaxing randm walk generatin and aviding grid search tuning fr tw additinal parameters Cnducted experiments shwcase the perfrmance benefits which stem frm taking int accunt the dynamic aspect Nvelty: Als frm expliting RFM-augmented netwrks and learning nde representatins frm these First wrk bth in using (dynamic) nde representatins in CDR graphs fr churn predictin and First wrk in applying the RFM framewrk tgether with unsupervised and dynamic learning f nde representatins 17 Churn Predictin using Dynamic RFM-Augmented nde2vec
18 Future research Attempt capturing call dynamics in a mre sphisticated manner (e.g. the rdering f calls, their inter-event time distributin) Investigate the effect f different time granularities Explre whether priritizing mre recent dynamic netwrks imprves perfrmance 18 Churn Predictin using Dynamic RFM-Augmented nde2vec
19 Thank yu! Questins?
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